Andreas Zeiser

Adaptive eigenvalue computation: complexity estimates

This paper is concerned with the design and analysis of a fully adaptive eigenvalue solver for linear symmetric operators. After transforming the original problem into an equivalent one formulated on ℓ2, the space of square summable sequences, the problem becomes sufficiently well conditioned so that a gradient type iteration can be shown to reduce the error by some fixed factor per step. It then remains to realize these (ideal) iterations within suitable dynamically updated error tolerances. It is shown under which circumstances the adaptive scheme exhibits in some sense asymptotically optimal complexity.

Fast Matrix-Vector Multiplication in the Sparse-Grid Galerkin Method

Sparse grid discretization of higher dimensional partial differential equations is a means to break the curse of dimensionality. For classical sparse grids based on the one-dimensional hierarchical basis, a sophisticated algorithm has been devised to calculate the application of a vector to the Galerkin matrix in linear complexity, despite the fact that the matrix is not sparse. However more general sparse grid constructions have been recently introduced, e.g. based on multilevel finite elements, where the specified algorithms only have a log-linear scaling. This article extends the idea of the linear scaling algorithm to more general sparse grid spaces. This is achieved by abstracting the algorithm given in (Balder and Zenger, SIAM J. Sci. Comput. 17:631, 1996) from specific bases, thereby identifying the prerequisites for performing the algorithm. In this way one can easily adapt the algorithm to specific discretizations, leading for example to an optimal linear scaling algorithm in the case of multilevel finite element frames.

Perturbed preconditioned inverse iteration for operator eigenvalue problems with applications to adaptive wavelet discretization

In this paper we discuss an abstract iteration scheme for the calculation of the smallest eigenvalue of an elliptic operator eigenvalue problem. A short and geometric proof based on the preconditioned inverse iteration (PINVIT) for matrices (Knyazev and Neymeyr, SIAM J Matrix Anal 31:621–628, 2009) is extended to the case of operators. We show that convergence is retained up to any tolerance if one only uses approximate applications of operators which leads to the perturbed preconditioned inverse iteration (PPINVIT). We then analyze the Besov regularity of the eigenfunctions of the Poisson eigenvalue problem on a polygonal domain, showing the advantage of an adaptive solver to uniform refinement when using a stable wavelet base. A numerical example for PPINVIT, applied to the model problem on the L-shaped domain, is shown to reproduce the predicted behaviour.

Kinetic theory of the electron transport in the two photon photo emission at semiconductor surfaces

A theoretical description of ultrafast phonon induced electronic transport between surface and bulk states after optical excitation is presented. In particular, the influence of the electron transfer processes on two photon photo emission is evaluated.